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Claude-Code-Workflow/.claude/skills/skill-iter-tune/templates/eval-prompt.md
catlog22 9fb13ed6b0 Implement phases for skill iteration tuning: Evaluation, Improvement, and Reporting
- Added Phase 3: Evaluate Quality with steps for preparing context, constructing evaluation prompts, executing evaluation via CLI, parsing scores, and checking termination conditions.
- Introduced Phase 4: Apply Improvements to implement targeted changes based on evaluation suggestions, including agent execution and change documentation.
- Created Phase 5: Final Report to generate a comprehensive report of the iteration process, including score progression and remaining weaknesses.
- Established evaluation criteria in a new document to guide the evaluation process.
- Developed templates for evaluation and execution prompts to standardize input for the evaluation and execution phases.
2026-03-10 21:42:58 +08:00

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# Evaluation Prompt Template
Phase 03 使用此模板构造 ccw cli 提示词,让 Gemini 按多维度评估 skill 质量。
## Template
```
PURPOSE: Evaluate the quality of a workflow skill by examining both its definition files and the artifacts it produced when executed against a test scenario. Provide a structured multi-dimensional score with actionable improvement suggestions.
SKILL DEFINITION:
${skillContent}
TEST SCENARIO:
${testScenario.description}
Requirements: ${testScenario.requirements}
Success Criteria: ${testScenario.success_criteria}
ARTIFACTS PRODUCED:
${artifactsSummary}
EVALUATION CRITERIA:
${evaluationCriteria}
${previousEvalContext}
TASK:
1. Read all skill definition files and produced artifacts carefully
2. Score each dimension on 0-100 based on the evaluation criteria:
- Clarity (weight 0.20): Instructions unambiguous, well-structured, easy to follow
- Completeness (weight 0.25): All phases, edge cases, error handling covered
- Correctness (weight 0.25): Logic sound, data flow consistent, no contradictions
- Effectiveness (weight 0.20): Produces high-quality output for the test scenario
- Efficiency (weight 0.10): Minimal redundancy, appropriate context usage
3. Calculate weighted composite score
4. List top 3 strengths
5. List top 3-5 weaknesses with specific file:section references
6. Provide 3-5 prioritized improvement suggestions with concrete changes
MODE: analysis
EXPECTED OUTPUT FORMAT (strict JSON, no markdown wrapping):
{
"composite_score": <number 0-100>,
"dimensions": [
{ "name": "Clarity", "id": "clarity", "score": <0-100>, "weight": 0.20, "feedback": "<specific feedback>" },
{ "name": "Completeness", "id": "completeness", "score": <0-100>, "weight": 0.25, "feedback": "<specific feedback>" },
{ "name": "Correctness", "id": "correctness", "score": <0-100>, "weight": 0.25, "feedback": "<specific feedback>" },
{ "name": "Effectiveness", "id": "effectiveness", "score": <0-100>, "weight": 0.20, "feedback": "<specific feedback>" },
{ "name": "Efficiency", "id": "efficiency", "score": <0-100>, "weight": 0.10, "feedback": "<specific feedback>" }
],
"strengths": ["<strength 1>", "<strength 2>", "<strength 3>"],
"weaknesses": ["<weakness 1 with file:section reference>", "..."],
"suggestions": [
{
"priority": "high|medium|low",
"target_file": "<relative path to skill file>",
"description": "<what to change>",
"rationale": "<why this improves quality>",
"code_snippet": "<optional: suggested replacement content>"
}
],
"chain_scores": {
"<skill_name>": "<number 0-100, per-skill score — only present in chain mode>"
}
}
CONSTRAINTS:
- Be rigorous and specific — reference exact file paths and sections
- Each suggestion MUST include a target_file that maps to a skill file
- Focus suggestions on highest-impact changes first
- Do NOT suggest changes already addressed in previous iterations
- Output ONLY the JSON object, no surrounding text or markdown
```
## Variable Substitution
| Variable | Source | Description |
|----------|--------|-------------|
| `${skillContent}` | Same as execute-prompt.md | 完整 skill 文件内容 |
| `${testScenario.*}` | iteration-state.json | 测试场景信息 |
| `${artifactsSummary}` | Phase 03 reads artifacts/ dir | 产出物文件列表 + 内容摘要 |
| `${evaluationCriteria}` | specs/evaluation-criteria.md | 评分标准全文 |
| `${previousEvalContext}` | 历史迭代记录 | 前几轮评估摘要(避免重复建议) |
| `${chainContext}` | Phase 03 constructs | chain 模式下的链上下文信息 |
## previousEvalContext Construction
```javascript
// Build context from prior iterations to avoid repeating suggestions
const previousEvalContext = state.iterations.length > 0
? `PREVIOUS ITERATIONS (context for avoiding duplicate suggestions):
${state.iterations.map(iter => `
Iteration ${iter.round}: Score ${iter.evaluation?.score || 'N/A'}
Applied changes: ${iter.improvement?.changes_applied?.map(c => c.summary).join('; ') || 'none'}
Remaining weaknesses: ${iter.evaluation?.weaknesses?.slice(0, 3).join('; ') || 'none'}
`).join('')}
IMPORTANT: Focus on NEW issues or issues NOT adequately addressed in previous improvements.`
: '';
```
## chainContext Construction
```javascript
// Build chain context for evaluation (chain mode only)
const chainContext = state.execution_mode === 'chain'
? `CHAIN CONTEXT:
This skill chain contains ${state.chain_order.length} skills executed in order:
${state.chain_order.map((s, i) => `${i+1}. ${s}`).join('\n')}
Current evaluation covers the entire chain output.
Please provide per-skill quality scores in an additional "chain_scores" field.`
: '';
```
## artifactsSummary Construction
```javascript
// Read manifest.json if available, otherwise list files
const manifestPath = `${iterDir}/artifacts/manifest.json`;
let artifactsSummary;
if (fileExists(manifestPath)) {
const manifest = JSON.parse(Read(manifestPath));
artifactsSummary = manifest.artifacts.map(a =>
`- ${a.path}: ${a.description} (Phase ${a.phase})`
).join('\n');
} else {
// Fallback: list all files with first 200 lines each
const files = Glob(`${iterDir}/artifacts/**/*`);
artifactsSummary = files.map(f => {
const content = Read(f, { limit: 200 });
return `--- ${f.replace(iterDir + '/artifacts/', '')} ---\n${content}`;
}).join('\n\n');
}
```